DEVELOPMENTAL IMPACT OF TAXATION ON THE ECONOMIC PERFORMANCE OF SELECTED DEVELOPED ECONOMIES AROUND THE WORLD.
Bibliographic record
Abstract
Taxation, the primary component of fiscal policy, definitely influences economic production because, in industrialized nations, the government uses the money from taxpayers to build fundamental infrastructure like reliable electricity, well-maintained roads, and water supplies. Therefore, the aim of this study is to investigate the developmental effect of taxes on the economic performance of developed economies around the world. Panel VAR application revealed a short-term correlation between taxation and the economic performance of industrialized countries. While the fitted FMOLS reveals a significant positive impact of taxation and foreign direct investment (FDI) on the long-term economic performance of developed nations, suggesting that higher levels of taxation and FDI return contribute to greater economic performance in the world’s developed economies, the Hausman test specifies a random-effect regression model that confirms the significant positive impact of taxation and GNI on economic performance. As a result, the governments of industrialized nations should keep putting in place a sustainable tax system that is alluring enough to raise tax payments and promote the continuation of FDI and GNI growth, which would improve economic performance both now and in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".